Clustering and Classification of Time Series in Real-Time Strategy Games - A machine learning approach for mapping StarCraft II games to clusters of game state time series while limited by fog of war
Typ
Examensarbete på kandidatnivå
Program
Publicerad
2019
Författare
Enström, Olof
Hagström, Fredrik
Segerstedt, John
Viberg, Fredrik
Wartenberg, Arvid
Weber Fors, David
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Real-time strategy (RTS) games feature vast action spaces and incomplete information,
thus requiring lengthy training times for AI-agents to master them at the level of a
human expert. Based on the inherent complexity and the strategical interplay between
the players of an RTS game, it is hypothesized that data sets of played games exhibit
clustering properties as a result of the actions made by the players. These clusters
could potentially be used to optimize the training process of AI-agents, and gain
unbiased insight into the gameplay dynamics. In this thesis, a method is presented to
discern such clusters and classify an ongoing game according to which of these clusters
it most closely resembles, limited to the perspective of a single player. Six distinct
clusters have been found in StarCraft II using hierarchical clustering over time, all
of which depend on different combinations of game pieces and the timing of their
acquisitions in the game. An ongoing game can be classified, using neural networks
and random forests, as a member of some cluster with accuracies ranging from 83%
to 96% depending on the amount of information provided.
Beskrivning
Ämne/nyckelord
Classification problem , Cluster analysis , Hierarchical clustering , Machine learning , Neural network , Random forest , Real-time strategy , StarCraft II , Time series